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Probabilistic identification of surface recession patterns in heritage buildings based on digital photogrammetry

Abstract:

The deterioration of the built heritage is becoming a pressing issue in many countries. The assessment of such a degradation at large (building) scale is key for maintenance priorisation and decision making. This paper proposes a straightforward yet rigorous method to asses and predict the surface recession in heritage buildings. The method is based on a probabilistic Bayesian approach to identify the most plausible surface recession pattern using digital photogrammetry data. In particular, a set of candidate recession patterns are defined and ranked based on probabilities that measure the relative extent of support of the hypothesised models to the observed data. A real case study for a sixteenth century heritage building in Granada (Spain) is presented. The results show the efficiency of the proposed methodology in identifying not only the most suitable recession pattern for different parts of the building, but also the probability density functions of the basic geometry parameters representing the identified patterns, such as the depth and the height of the surface recession.